CN106900070A - A kind of mobile device multiple utility program data transfer energy consumption optimization method - Google Patents

A kind of mobile device multiple utility program data transfer energy consumption optimization method Download PDF

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Publication number
CN106900070A
CN106900070A CN201710013989.3A CN201710013989A CN106900070A CN 106900070 A CN106900070 A CN 106900070A CN 201710013989 A CN201710013989 A CN 201710013989A CN 106900070 A CN106900070 A CN 106900070A
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China
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moment
energy consumption
mobile device
tail
data transfer
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CN201710013989.3A
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Chinese (zh)
Inventor
范文浩
刘元安
徐飞
吴帆
张洪光
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北京邮电大学
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Priority to CN201710013989.3A priority Critical patent/CN106900070A/en
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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W72/00Local resource management, e.g. wireless traffic scheduling or selection or allocation of wireless resources
    • H04W72/12Dynamic Wireless traffic scheduling ; Dynamically scheduled allocation on shared channel
    • H04W72/1205Schedule definition, set-up or creation
    • H04W72/1252Schedule definition, set-up or creation based on load
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W72/00Local resource management, e.g. wireless traffic scheduling or selection or allocation of wireless resources
    • H04W72/12Dynamic Wireless traffic scheduling ; Dynamically scheduled allocation on shared channel
    • H04W72/1205Schedule definition, set-up or creation
    • H04W72/1257Schedule definition, set-up or creation based on resource usage policy
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W72/00Local resource management, e.g. wireless traffic scheduling or selection or allocation of wireless resources
    • H04W72/12Dynamic Wireless traffic scheduling ; Dynamically scheduled allocation on shared channel
    • H04W72/1278Transmission of control information for scheduling
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02DCLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THIR OWN ENERGY USE
    • Y02D70/00Techniques for reducing energy consumption in wireless communication networks

Abstract

The invention discloses a kind of mobile device multiple utility program data transfer energy consumption optimization method, including:Linear segment prediction is carried out to the former time series that the moment reached by data transfer constitutes using difference ARMA model, the residual sequence of the former time series is obtained;The residual sequence is predicted using neural network model, determines hybrid model for short-term load forecasting;According to the hybrid model for short-term load forecasting, next data transfer moment at the first moment in the former time series of prediction, as the second moment, and corresponding adjustment is carried out to the level state of mobile device according to the first moment and its corresponding tail time and with the second moment magnitude relationship.By dynamic adjustment tail time length to reduce tail energy consumption, while the Pre-handoff mobile device level state when data transfer request next time is reached, reduces transmission delay, user experience is improve.

Description

A kind of mobile device multiple utility program data transfer energy consumption optimization method

Technical field

The present invention relates to be moved under cellular network radio resource control protocol (Radio Resource Control, RRC) Device data transmits energy optimization technical field, particularly relates to a kind of mobile device multiple utility program data transfer energy consumption optimization side Method.

Background technology

Computer technology is developed rapidly with the communication technology, promotes the quantity of mobile device with smart mobile phone as representative fast Surge length.At the same time, the continuous lifting of mobile device processor ability and the continuous growth of cellular bandwidth, more promote The fast development of mobile applications type and quantity.Various, the feature-rich various application programs of quantity are being people's Life offers convenience while with enjoyment, also greatly consumes the energy of mobile device.However, mobile device battery capacity Development speed and limited battery durable ability but turn into the bottleneck of influence enhancing mobile applications Consumer's Experience.Therefore, drop The energy consumption of low mobile device turns into problem in the urgent need to address.The energy consumption of mobile device data transmitting procedure is led in cellular network It is subjected to the control of the wireless MAC protocols such as RRC (Radio Resource Control), data radio electricity after the end of transmission Putting down will not immediately drop to low level state, and be to maintain the high level of a period of time, complete but still to keep high in data transfer In the time of level state, if without subsequent data transmission, radio level is just transformed into low level from high level state.This section of nothing Data transfer but kept for the time of high level state be referred to as the tail time (tail time), the energy that is caused in this period wave Take referred to as tail energy (tail energy).The signal overhead for being introduced for avoiding Radio Access Network too high of tail time, but If occurring the excessive tail time in data transmission procedure, capacity usage ratio will be greatly reduced.Therefore how to be effectively reduced The influence of tail energy turns into the key for solving the problems, such as mobile device data transmission energy optimization in cellular network.

By taking RadioJockey as an example, the existing energy optimization scheme based on tail time tuning is built upon greatly single kind It is special only for the data transfer of each class application program by dividing application program species on the basis of application data transmission The point simple forecast data transfer moment determines when cut off the tail time, although can reach reduction tail energy consumption mesh to a certain extent , but still inevitably bring other problemses.First, for single kind application program energy optimization does not meet simultaneously Mobile device runs the actual conditions of various multiple application programs simultaneously, and the single data transfer feature of application program simply makes Forecast model simplifies and causes the prediction accuracy to reduce;Secondly, frequently cutting off the tail time can cause unnecessary state to switch product Raw more multimode lifting energy consumption, while state lifting is time-consuming to cause propagation delay time, reduces user experience.

The content of the invention

In view of this, it is an object of the invention to propose a kind of dynamic adjustment tail time length to reduce tail energy consumption, while The Pre-handoff mobile device level state when data transfer request next time is reached, reduces transmission delay, improves user's body The data transfer energy consumption optimization method of degree of testing.

Based on a kind of mobile device multiple utility program data transfer energy consumption optimization method that the above-mentioned purpose present invention is provided, bag Include:

The former time series that the moment reached by data transfer constitutes is carried out using difference ARMA model Linear segment prediction, obtains the residual sequence of the former time series;

The residual sequence is predicted using neural network model, determines hybrid model for short-term load forecasting;

According to the hybrid model for short-term load forecasting, next data transfer moment at the first moment in the former time series of prediction, as Second moment, and according to the first moment and its corresponding tail time and with the second moment magnitude relationship to the electricity of mobile device Level state carries out corresponding adjustment.

Further, the level state to mobile device carry out it is corresponding adjustment be specially:

When tail time corresponding with its at first moment and during less than or equal to second moment, then when retaining tail Between;

When tail time corresponding with its at first moment and during more than second moment, judge the actual tail saved Magnitude relationship between energy consumption and state lifting energy consumption, if the actual tail energy consumption saved lifts energy consumption less than state, protects The tail time is stayed, if the actual tail energy consumption saved lifts energy consumption more than state, mobile device power save mode is dropped into, and Mobile device is risen into dedicated channel at the difference corresponding moment of tail time corresponding with first moment at the second moment State.

Further, also including carrying out error correction to the second moment, specially:

According to the second moment in former time series the difference at corresponding 3rd moment and the difference at second moment, obtain Predicated error;

When the value of the predicated error is positive number, if the predicated error is less than the value of the tail time at the first moment, Mobile device is then risen into dedicated channel status and the state is maintained, if the predicated error is more than the tail time at the first moment Value when, contrast transmission energy consumption and both sides state lifted energy consumption size, if transmission energy consumption it is larger, mobile device is switched to Forward access channel status, and at the difference corresponding moment of tail time corresponding with the first moment at the 3rd moment, movement is set It is standby to rise to dedicated channel status;

When the value of the predicated error is negative, second moment is repaiied before data transfer request arrival Just, the value for making the predicated error is positive number.

Further, the determination process of the hybrid model for short-term load forecasting is specifically included:

Whether the former time series of inspection is steady, if former time series is unstable, difference is carried out to former time series, until Obtain the stationary sequence of former time series;

Asking auto-correlation and partial autocorrelation function carries out Model Identification;

The valued space (p, q) of exhaustive parameter p and q, the corresponding parameter of each group of fitting (p, q);

Corresponding information criterion AIC is calculated, and selects the minimum valued space (p, q) of AIC values to be set up as model parameter Model carries out linear segment prediction;

Residual sequence, input residual error learning sample are calculated, and calculates output and the reverse propagated error of unit, according to Reverse propagated error adjusts weights and threshold value according to BP model modified weights formula, and selection meets the weights and threshold value of required precision To residual sequence modeling and forecasting, finally give and meet forecast model.

From the above it can be seen that the mobile device multiple utility program data transfer energy consumption optimization side that the present invention is provided Method, including:The former time series that the moment reached by data transfer constitutes is carried out using difference ARMA model Linear segment prediction, obtains the residual sequence of the former time series;The residual sequence is carried out using neural network model Prediction, determines hybrid model for short-term load forecasting;According to the hybrid model for short-term load forecasting, next data at the first moment in the former time series of prediction Transmission time, as the second moment, and according to the first moment and its corresponding tail time and with the second moment magnitude relationship Level state to mobile device carries out corresponding adjustment.By dynamic adjustment tail time length to reduce tail energy consumption, while Pre-handoff mobile device level state when data transfer request is reached next time, reduces transmission delay, improves user's body Degree of testing.

Brief description of the drawings

Fig. 1 is the flow chart of one embodiment of mobile device multiple utility program data transfer energy consumption optimization method of the present invention;

Fig. 2 is the time sequence of one embodiment of mobile device multiple utility program data transfer energy consumption optimization method of the present invention Row prediction flow chart.

Specific embodiment

To make the object, technical solutions and advantages of the present invention become more apparent, below in conjunction with specific embodiment, and reference Accompanying drawing, the present invention is described in more detail.

A kind of mobile device multiple utility program data transfer energy consumption optimization method that the present invention is provided, including:Using difference ARMA model carries out linear segment prediction to the former time series that the moment reached by data transfer constitutes, and obtains The residual sequence of the former time series;The residual sequence is predicted using neural network model, it is determined that compound prediction Model;According to the hybrid model for short-term load forecasting, next data transfer moment at the first moment in the former time series of prediction, as second Moment, and according to the first moment and its corresponding tail time and with the second moment magnitude relationship to the level shape of mobile device State carries out corresponding adjustment.

The mobile device multiple utility program data transfer energy consumption optimization method that the present invention is provided, by the dynamic adjustment tail time Length is to reduce tail energy consumption, while the Pre-handoff mobile device level state when data transfer request next time is reached, subtracts Few transmission delay, improves user experience.

As shown in figure 1, being one embodiment of mobile device multiple utility program data transfer energy consumption optimization method of the present invention Flow chart, comprise the following steps:

Step 101:The former time constituted to the moment reached by data transfer using difference ARMA model Sequence carries out linear segment prediction, obtains the residual sequence of the former time series.

Step 102:The residual sequence is predicted using neural network model, determines hybrid model for short-term load forecasting.

Step 103:According to the hybrid model for short-term load forecasting, in the former time series of prediction during next data transfer at the first moment Carve, as the second moment, and according to the first moment and its corresponding tail time and with the second moment magnitude relationship to movement The level state of equipment carries out corresponding adjustment.

The method of the present embodiment adjusts tail time length to reduce tail energy consumption by dynamic, while in next data transfer Pre-handoff mobile device level state when request is reached, reduces transmission delay, improves user experience.

In order that technical scheme is more easily understood, with reference to specific embodiment with to technology of the invention Scheme is illustrated.

The cellular network data transmission series model of SmartTT, namely hybrid model for short-term load forecasting are given first.Define data set D={ d1,d2,d3,……,di,……,dnIt is the data sequence that is constituted of data transfer request for reaching at each moment, time Collection T={ t1,t2,t3,……,ti,……,tnBy in data set D corresponding data transmit a request to the time constituted up to the moment Sequence, namely former time series.Cellular network interface has fixed value in each RRC state power during data transfer, and adjacent Time interval between two data transfers determines how cellular network interface is changed and during tail between different RRC states Between length, therefore the data item t in time collection TiTail energy consumption E will be directly affectedtail.In the technology of above-mentioned model, the present invention The SmartTT of technical scheme includes time series forecasting, the adjustment of tail time, the part of error correction three.

Time series forecasting:

Actual time series generally has linear processes compound characteristics simultaneously, and (difference autoregression is moved ARIMA models Dynamic averaging model) and BP models (neural network model) respectively in terms of linear and Nonlinear Time Series prediction with significant advantage, Therefore the present invention uses ARIMA and BP hybrid model for short-term load forecasting to time series T={ t1,t2,t3,……,ti,……,tnCarry out Prediction.Have following it is assumed that time series T={ t1,t2,t3,……,ti,……,tnBy linear segment LiWith non-linear partial Ni Composition:

ti=Li+Ni (1)

Time series T is predicted first with ARIMA models, it is assumed that predicted value is L 'i, former time series with predict the outcome Between residual error be assumed to be ei

ei=ti-L′i (2)

Residual error eiReflect tiIn non-linear relation, using BP models to eiPrediction, it is assumed that predicted value is N 'i, then time The final predicted value of sequence T is:

t′i=L 'i+N′i (3)

Specific time series forecasting flow is as shown in Fig. 2 Fig. 2 is mobile device multiple utility program data transfer of the present invention The time series forecasting flow chart of one embodiment of energy consumption optimization method.Key step is as follows:

Step one:Whether Check-Out Time sequence T is steady, and the unstable difference that carries out is until obtaining stationary sequence;

Step 2:Auto-correlation and the partial autocorrelation function is asked to carry out Model Identification according to formula (4) and (5);

Step 3:The valued space (p, q) of exhaustive parameter p and q, each group (p, q) corresponding parameter is fitted by formula (6)

Wherein, εiIt is 0 to obey average, and variance is constant σ2The normal distribution of (residual variance after fitting)

Step 4:Corresponding AIC is calculated according to formula (7);

AIC=N log σ2+(p+q+1)log N (7)

Step 5:Selection corresponding A IC values minimum (p, q) set up model as model parameter carries out linear segment prediction;

Step 6:Residual sequence is calculated according to formula (2);

Step 7:Using BP model prediction residual sequences, weights ω is initialized firstij=Random ();

Step 8:Input residual sequence learning sample;

Step 9:Output and the reverse propagated error of unit are calculated by formula (8), formula (9) (10) respectively;

Step 10:Weights and threshold value are adjusted according to BP model modified weights formula according to reverse propagated error;

Step 11:Selection meets the weights and threshold value of required precision to residual sequence modeling and forecasting;

Step 12:Final hybrid model for short-term load forecasting is obtained by formula (3) combining step five and step 11 two parts.

The tail time adjusts:

RRC agreements specify that cellular network radio level is in high level state DCH, data transfer knot in data transfer The follow-up no data transmission of Shu Houruo, then drop to FACH state after one section of set time α of level maintenance DCH states;If it is follow-up after Continuous no data transmission, then finally drop to IDLE state, and begin when no data is transmitted after maintaining mono- section of set time β of FACH The state is kept eventually;When carrying out data transmission again, radio level is promoted to DCH states and carries out data from IDLE state again Transmission.By the protocol integrated test system, DCH states and FACH state level power are fixed, if respectively pDCHAnd pFACH;By IDLE state The state hoisting power and time delay for being promoted to DCH states are fixed, if respectively pproAnd tdelay.Assuming that data transmission period sequence {t1,t2,t3,……,ti,……,tnIt is corresponding prediction value sequence be { t '1,t′2,t′3,……,t′i,……,t′n, then without The tail time adjustment process of SmartTT is as follows under error correction:

(1) mobile device level state initialization, i.e., level state is IDLE when initial no data is transmitted;

(2) when level state is promoted to DCH carries out a data transfer, next number of times is predicted using hybrid model for short-term load forecasting According to transmission arrival time t 'i.If t 'i≤ti-1+tdelay, retaining the tail time, otherwise level state drops to IDLE immediately;

(3) as t 'i>ti-1+tdelay, there is following three kinds of situations again:

①ti-1+tdelay<t′i≤ti-1

②ti-1+α<t′i≤ti-1+α+β

③t′i>ti-1+α+β

Balance is needed to consider the tail energy consumption E that the adjustment of tail time is saved in the case of three kindstailThe state lifting energy for bringing therewith Consumption Epro, above-mentioned three kinds of situations are corresponded to, tail energy consumption is respectively with state lifting energy consumption:

Etail=pDCH*(t′i-ti-1)

Epro=ppro*tdelay

Etail=pDCH*α+pFACH*(t′i-ti-1-α)

Epro=ppro*tdelay

Etail=pDCH*α+pFACH

Epro=ppro*tdelay

If the actual tail energy consumption E for savingtailLess than the state lifting energy consumption E for producing therewithpro, retain the tail time, otherwise electricity Level state drops to IDLE immediately, while in advance in t 'i-tdelayMoment starts to be promoted to DCH states and carries out data transfer preparation, Avoid causing propagation delay time because of state lifting.

Error correction:

Ideally predicted value t 'iAccurately, SmartTT is effectively reduced tail energy consumption and state lifting energy consumption and avoids passing T ' under defeated time delay, but actual conditionsiAccurate Prediction every time is not ensured that, this can undoubtedly be produced to the performance of SmartTT Influence.Therefore SmartTT introduces corresponding error revising strategies and is modified in terms of following two:

Predicted value t 'iLess than normal, radio level has fulfiled state lifting ahead of schedule, but data transfer does not start, and produces many Remaining data transfer energy consumption;

Predicted value t 'iBigger than normal, radio level not yet completion status is lifted, but data transfer request has arrived at, and causes Propagation delay time influences user experience.

Assuming that actual prediction error is δi, then have:

δi=ti-t′i

δ is understood by above formulaiIt is also a time series, equally available hybrid model for short-term load forecasting is predicted, it is assumed that predicted value is δ′i, then the error correction in the case of two kinds is as follows, and specific optimisation strategy is as shown in table 1:

δ′i>0, predicted value is less than normal, and DCH states are promoted in advance causes unnecessary transmission energy consumption, is set to Etrans, specifically again Two following situations can be divided into:

δ′i<tdelay, DCH states are maintained after state lifting, start data transfer until data transfer request is reached;

δ′i>tdelayIf maintaining DCH states to be reached until data transfer request after state lifting, excessive biography may be wasted Delivery of energy consumes;If being re-lowered to IDLE state and being promoted to DCH states again before data transfer request arrival, can cause twice Unnecessary state lifting energy consumption.Therefore E need to be comparedtransWith two next states lifting energy consumption EproSize decide whether to continue waiting for. Etrans=pDCH*δ′i Epro=ppro*tdelay

If Etrans>2*Epro, IDLE state is switched to, and in t 'i+δ′i-tdelayMoment, state was promoted to DCH again;Instead Maintenance DCH states until data transfer request reach start data transfer.

δ′i<0, predicted value is bigger than normal, and data transfer request has been reached but not yet completion status lifting, causes certain transmission Time delay.For this kind of situation, to avoid excessively influenceing Consumer's Experience SmartTT to be adjusted again using prevention in advance rather than after occurring Whole strategy, once find δ 'i<0 then immediately by t 'iIt is modified to t 'i-|δ′i|, afterwards by normal tail time adjustable strategies in 2 Operated.

Energy optimization strategy of the table 1 with error correction

Relative to the existing energy optimization strategy based on tail time tuning, the present invention has the following advantages:

From mobile device practical operation situation, premised on multiple utility program parallel data transmission, using compound pre- Survey model to carry out data transmission predicting constantly, it is to avoid the single data transfer feature of application program simply causes that forecast model simplifies, Improve prediction accuracy.The unnecessary state brought in view of the adjustment of tail time lifts energy consumption, it is to avoid reduce tail energy consumption simply And cause other energy consumption expenses, improve energy optimization rate;In view of propagation delay time caused by switching between state, by advance Data transfer preparation is carried out in state lifting, it is to avoid influence user experience;Introduce error revising strategies, and mistake excessive to predicted value Small two kinds of situations are adjusted respectively, and the degree of accuracy is ensured to a greater extent, improve energy optimization strategy performance of the invention.

It should be noted that the statement of all uses " first " and " second " is for differentiation two in the embodiment of the present invention The entity of individual same names non-equal or the parameter of non-equal, it is seen that " first " " second " should not only for the convenience of statement The restriction to the embodiment of the present invention is interpreted as, subsequent embodiment is no longer illustrated one by one to this.

Those of ordinary skill in the art should be understood:The discussion of any of the above embodiment is exemplary only, not It is intended to imply that the scope of the present disclosure (including claim) is limited to these examples;Under thinking of the invention, above example Or can also be combined between the technical characteristic in different embodiments, step can be realized with random order, and be existed such as Many other changes of upper described different aspect of the invention, for simplicity, they are provided not in details.

In addition, to simplify explanation and discussing, and in order to obscure the invention, can in the accompanying drawing for being provided To show or can not show to be connected with the known power ground of integrated circuit (IC) chip and other parts.Furthermore, it is possible to Device is shown in block diagram form, to avoid obscuring the invention, and this have also contemplated that following facts, i.e., on this The details of the implementation method of a little block diagram arrangements is to depend highly on to implement platform of the invention (that is, these details should It is completely in the range of the understanding of those skilled in the art).Elaborating that detail (for example, circuit) is of the invention to describe In the case of exemplary embodiment, it will be apparent to those skilled in the art that can be without these details In the case of or implement the present invention in the case that these details are changed.Therefore, these descriptions are considered as explanation Property rather than restricted.

Although invention has been described to have been incorporated with specific embodiment of the invention, according to retouching above State, many replacements of these embodiments, modification and modification will be apparent for those of ordinary skills.Example Such as, other memory architectures (for example, dynamic ram (DRAM)) can use discussed embodiment.

Embodiments of the invention be intended to fall within the broad range of appended claims it is all such replace, Modification and modification.Therefore, all any omission, modification, equivalent, improvement within the spirit and principles in the present invention, made Deng should be included within the scope of the present invention.

Claims (4)

1. a kind of mobile device multiple utility program data transfer energy consumption optimization method, it is characterised in that including:
The former time series that the moment reached by data transfer constitutes is carried out linearly using difference ARMA model Fractional prediction, obtains the residual sequence of the former time series;
The residual sequence is predicted using neural network model, determines hybrid model for short-term load forecasting;
According to the hybrid model for short-term load forecasting, next data transfer moment at the first moment in the former time series of prediction, as second Moment, and according to the first moment and its corresponding tail time and with the second moment magnitude relationship to the level shape of mobile device State carries out corresponding adjustment.
2. method according to claim 1, it is characterised in that the level state to mobile device carries out corresponding tune It is whole to be specially:
When tail time corresponding with its at first moment and during less than or equal to second moment, then retain the tail time;
When tail time corresponding with its at first moment and during more than second moment, judge the actual tail energy consumption saved With the magnitude relationship between state lifting energy consumption, if the actual tail energy consumption saved lifts energy consumption less than state, retain tail Time, if the actual tail energy consumption saved lifts energy consumption more than state, mobile device is dropped into power save mode, and the Mobile device is risen to dedicated channel status by the difference corresponding moment of tail time corresponding with first moment at two moment.
3. method according to claim 1, it is characterised in that also including carrying out error correction to the second moment, specially:
According to the second moment in former time series the difference at corresponding 3rd moment and the difference at second moment, predicted Error;
When the value of the predicated error is positive number, if the predicated error is less than the value of the tail time at the first moment, will Mobile device rises to dedicated channel status and maintains the state, if the predicated error is more than the value of the tail time at the first moment When, contrast transmission energy consumption and both sides state lifted energy consumption size, if transmission energy consumption it is larger, by mobile device switch to before to Access channel state, and at the difference corresponding moment of tail time corresponding with the first moment at the 3rd moment, by mobile device It is upgraded to dedicated channel status;
When the value of the predicated error is negative, second moment is modified before data transfer request arrival, made The value of the predicated error is positive number.
4. method according to claim 1, it is characterised in that the determination process of the hybrid model for short-term load forecasting is specifically included:
Whether the former time series of inspection is steady, if former time series is unstable, difference is carried out to former time series, until obtaining The stationary sequence of former time series;
Asking auto-correlation and partial autocorrelation function carries out Model Identification;
The valued space (p, q) of exhaustive parameter p and q, the corresponding parameter of each group of fitting (p, q);
Corresponding information criterion AIC is calculated, and the valued space (p, q) for selecting AIC values minimum sets up model as model parameter Carry out linear segment prediction;
Residual sequence, input residual error learning sample are calculated, and calculates output and the reverse propagated error of unit, according to reverse Propagated error adjusts weights and threshold value according to BP model modified weights formula, and selection meets the weights and threshold value of required precision to residual Difference sequence modeling and forecasting, finally gives and meets forecast model.
CN201710013989.3A 2017-01-09 2017-01-09 A kind of mobile device multiple utility program data transfer energy consumption optimization method CN106900070A (en)

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余飞: "3G移动网络终端能耗问题研究", 《中国优秀硕士学位论文全文数据库信息科技辑》 *
李明丽: "智能手机在3G和LTE网络中的尾能耗研究与优化", 《中国优秀硕士学位论文全文数据库信息科技辑》 *
熊志斌: "基于ARIMA与神经网络集成的GDP时间序列预测研究", 《数理统计与管理》 *
高岭,陈艳,王海,任杰: "基于时间序列的蜂窝网络能量优化方法", 《北京邮电大学学报》 *

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* Cited by examiner, † Cited by third party
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WO2019062413A1 (en) * 2017-09-30 2019-04-04 Oppo广东移动通信有限公司 Method and apparatus for managing and controlling application program, storage medium, and electronic device
CN107786963A (en) * 2017-10-26 2018-03-09 北京邮电大学 The migration strategy of calculating task in a kind of self-organizing network

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